Overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. In the context of toxicology, this can happen when a model learns the training data too well, capturing not only the intended patterns but also the random fluctuations present in the data. As a result, the model performs excellently on training data but poorly on unseen data, which is critical for predictive toxicology where generalization to new compounds is essential.
